Using species distribution models to describe essential fish habitat in Alaska
Bibliographic record
Abstract
Describing essential habitat is an important step toward understanding and conserving harvested species in ecosystem-based fishery management. Using data from fishery-independent ichthyoplankton, groundfish surveys, and commercial fisheries observer data, we utilized species distribution modeling techniques to predict habitat-based spatial distributions of federally managed species in Alaska. The distribution and abundance maps were used to refine existing essential fish habitat descriptions for the region. In particular, we used maximum entropy and generalized additive modeling to delineate distribution and abundance of early (egg, larval, and pelagic juvenile) and later (settled juvenile and adult) life history stages of groundfishes and crabs across multiple seasons in three large marine ecosystems (Gulf of Alaska, eastern Bering Sea, and Aleutian Islands) and the northern Bering Sea. We present a case study, featuring Kamchatka flounder (Atheresthes evermanni), from the eastern and northern Bering Sea to represent the >400 habitat-based distribution maps generated for more than 80 unique species–region–season–life-stage combinations. The results of these studies will be used to redescribe essential habitat of federally managed fishes and crabs in Alaska.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".